In RL as probabilistic inference, why do we take a probability to be $\exp(r(s_t, a_t))$?
After doing some further reading, it turns out that negative rewards are an assumption for this distribution to hold. However, the author notes that as long as you don't receive a reward of infinity for any action then it is possible to re-scale your rewards by subtracting the maximum value of your potential rewards so that they are always negative.